Overview

Dataset statistics

Number of variables32
Number of observations119210
Missing cells0
Missing cells (%)0.0%
Duplicate rows8704
Duplicate rows (%)7.3%
Total size in memory105.6 MiB
Average record size in memory928.7 B

Variable types

Categorical16
Numeric14
Unsupported1
DateTime1

Alerts

Dataset has 8704 (7.3%) duplicate rowsDuplicates
agent is highly overall correlated with hotelHigh correlation
arrival_date_month is highly overall correlated with arrival_date_week_numberHigh correlation
arrival_date_week_number is highly overall correlated with arrival_date_monthHigh correlation
assigned_room_type is highly overall correlated with reserved_room_typeHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
hotel is highly overall correlated with agentHigh correlation
is_canceled is highly overall correlated with reservation_statusHigh correlation
market_segment is highly overall correlated with distribution_channelHigh correlation
reservation_status is highly overall correlated with is_canceledHigh correlation
reserved_room_type is highly overall correlated with assigned_room_typeHigh correlation
children is highly imbalanced (80.6%)Imbalance
babies is highly imbalanced (97.1%)Imbalance
meal is highly imbalanced (53.5%)Imbalance
distribution_channel is highly imbalanced (63.2%)Imbalance
is_repeated_guest is highly imbalanced (79.8%)Imbalance
reserved_room_type is highly imbalanced (56.3%)Imbalance
deposit_type is highly imbalanced (65.3%)Imbalance
customer_type is highly imbalanced (50.6%)Imbalance
required_car_parking_spaces is highly imbalanced (85.4%)Imbalance
previous_cancellations is highly skewed (γ1 = 24.44392359)Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 23.53955539)Skewed
country is an unsupported type, check if it needs cleaning or further analysisUnsupported
lead_time has 6264 (5.3%) zerosZeros
stays_in_weekend_nights has 51895 (43.5%) zerosZeros
stays_in_week_nights has 7572 (6.4%) zerosZeros
previous_cancellations has 112731 (94.6%) zerosZeros
previous_bookings_not_canceled has 115597 (97.0%) zerosZeros
booking_changes has 101232 (84.9%) zerosZeros
agent has 16280 (13.7%) zerosZeros
company has 112442 (94.3%) zerosZeros
days_in_waiting_list has 115517 (96.9%) zerosZeros
adr has 1810 (1.5%) zerosZeros
total_of_special_requests has 70201 (58.9%) zerosZeros

Reproduction

Analysis started2025-11-22 08:00:12.524285
Analysis finished2025-11-22 08:00:48.881127
Duration36.36 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

hotel
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
City Hotel
79163 
Resort Hotel
40047 

Length

Max length12
Median length10
Mean length10.671873
Min length10

Characters and Unicode

Total characters1272194
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
City Hotel79163
66.4%
Resort Hotel40047
33.6%

Length

2025-11-22T13:30:49.022061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:49.107690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hotel119210
50.0%
city79163
33.2%
resort40047
 
16.8%

Most occurring characters

ValueCountFrequency (%)
t238420
18.7%
o159257
12.5%
e159257
12.5%
119210
9.4%
H119210
9.4%
l119210
9.4%
C79163
 
6.2%
i79163
 
6.2%
y79163
 
6.2%
R40047
 
3.1%
Other values (2)80094
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1272194
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t238420
18.7%
o159257
12.5%
e159257
12.5%
119210
9.4%
H119210
9.4%
l119210
9.4%
C79163
 
6.2%
i79163
 
6.2%
y79163
 
6.2%
R40047
 
3.1%
Other values (2)80094
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1272194
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t238420
18.7%
o159257
12.5%
e159257
12.5%
119210
9.4%
H119210
9.4%
l119210
9.4%
C79163
 
6.2%
i79163
 
6.2%
y79163
 
6.2%
R40047
 
3.1%
Other values (2)80094
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1272194
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t238420
18.7%
o159257
12.5%
e159257
12.5%
119210
9.4%
H119210
9.4%
l119210
9.4%
C79163
 
6.2%
i79163
 
6.2%
y79163
 
6.2%
R40047
 
3.1%
Other values (2)80094
 
6.3%

is_canceled
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
0
75011 
1
44199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
075011
62.9%
144199
37.1%

Length

2025-11-22T13:30:49.209776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:49.292910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
075011
62.9%
144199
37.1%

Most occurring characters

ValueCountFrequency (%)
075011
62.9%
144199
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
075011
62.9%
144199
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
075011
62.9%
144199
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
075011
62.9%
144199
37.1%

lead_time
Real number (ℝ)

Zeros 

Distinct479
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.10923
Minimum0
Maximum737
Zeros6264
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:49.387890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median69
Q3161
95-th percentile320
Maximum737
Range737
Interquartile range (IQR)143

Descriptive statistics

Standard deviation106.87545
Coefficient of variation (CV)1.0265704
Kurtosis1.6943723
Mean104.10923
Median Absolute Deviation (MAD)60
Skewness1.3458092
Sum12410861
Variance11422.362
MonotonicityNot monotonic
2025-11-22T13:30:49.507639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06264
 
5.3%
13445
 
2.9%
22065
 
1.7%
31815
 
1.5%
41710
 
1.4%
51563
 
1.3%
61444
 
1.2%
71329
 
1.1%
81138
 
1.0%
121079
 
0.9%
Other values (469)97358
81.7%
ValueCountFrequency (%)
06264
5.3%
13445
2.9%
22065
 
1.7%
31815
 
1.5%
41710
 
1.4%
51563
 
1.3%
61444
 
1.2%
71329
 
1.1%
81138
 
1.0%
9991
 
0.8%
ValueCountFrequency (%)
7371
 
< 0.1%
7091
 
< 0.1%
62917
< 0.1%
62630
< 0.1%
62217
< 0.1%
61517
< 0.1%
60817
< 0.1%
60530
< 0.1%
60117
< 0.1%
59417
< 0.1%

arrival_date_year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
2016
56623 
2017
40620 
2015
21967 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters476840
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
201656623
47.5%
201740620
34.1%
201521967
 
18.4%

Length

2025-11-22T13:30:49.596228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:49.637507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
201656623
47.5%
201740620
34.1%
201521967
 
18.4%

Most occurring characters

ValueCountFrequency (%)
2119210
25.0%
0119210
25.0%
1119210
25.0%
656623
11.9%
740620
 
8.5%
521967
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)476840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2119210
25.0%
0119210
25.0%
1119210
25.0%
656623
11.9%
740620
 
8.5%
521967
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)476840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2119210
25.0%
0119210
25.0%
1119210
25.0%
656623
11.9%
740620
 
8.5%
521967
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)476840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2119210
25.0%
0119210
25.0%
1119210
25.0%
656623
11.9%
740620
 
8.5%
521967
 
4.6%

arrival_date_month
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.1 MiB
August
13861 
July
12644 
May
11780 
October
11147 
April
11078 
Other values (7)
58700 

Length

Max length9
Median length7
Mean length5.9026927
Min length3

Characters and Unicode

Total characters703660
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly

Common Values

ValueCountFrequency (%)
August13861
11.6%
July12644
10.6%
May11780
9.9%
October11147
9.4%
April11078
9.3%
June10929
9.2%
September10500
8.8%
March9768
8.2%
February8052
6.8%
November6771
5.7%
Other values (2)12680
10.6%

Length

2025-11-22T13:30:49.690129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august13861
11.6%
july12644
10.6%
may11780
9.9%
october11147
9.4%
april11078
9.3%
june10929
9.2%
september10500
8.8%
march9768
8.2%
february8052
6.8%
november6771
5.7%
Other values (2)12680
10.6%

Most occurring characters

ValueCountFrequency (%)
e95447
13.6%
r78048
 
11.1%
u65268
 
9.3%
b43229
 
6.1%
a41442
 
5.9%
y38397
 
5.5%
t35508
 
5.0%
J29494
 
4.2%
c27674
 
3.9%
A24939
 
3.5%
Other values (16)224214
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)703660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e95447
13.6%
r78048
 
11.1%
u65268
 
9.3%
b43229
 
6.1%
a41442
 
5.9%
y38397
 
5.5%
t35508
 
5.0%
J29494
 
4.2%
c27674
 
3.9%
A24939
 
3.5%
Other values (16)224214
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)703660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e95447
13.6%
r78048
 
11.1%
u65268
 
9.3%
b43229
 
6.1%
a41442
 
5.9%
y38397
 
5.5%
t35508
 
5.0%
J29494
 
4.2%
c27674
 
3.9%
A24939
 
3.5%
Other values (16)224214
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)703660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e95447
13.6%
r78048
 
11.1%
u65268
 
9.3%
b43229
 
6.1%
a41442
 
5.9%
y38397
 
5.5%
t35508
 
5.0%
J29494
 
4.2%
c27674
 
3.9%
A24939
 
3.5%
Other values (16)224214
31.9%

arrival_date_week_number
Real number (ℝ)

High correlation 

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.163376
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:49.751119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median28
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.601107
Coefficient of variation (CV)0.5007149
Kurtosis-0.98542287
Mean27.163376
Median Absolute Deviation (MAD)11
Skewness-0.010198696
Sum3238146
Variance184.99011
MonotonicityNot monotonic
2025-11-22T13:30:50.037355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
333576
 
3.0%
303082
 
2.6%
323041
 
2.6%
343039
 
2.5%
182923
 
2.5%
212853
 
2.4%
282843
 
2.4%
172803
 
2.4%
202781
 
2.3%
292763
 
2.3%
Other values (43)89506
75.1%
ValueCountFrequency (%)
11045
0.9%
21216
1.0%
31318
1.1%
41485
1.2%
51385
1.2%
61507
1.3%
72102
1.8%
82212
1.9%
92109
1.8%
102142
1.8%
ValueCountFrequency (%)
531811
1.5%
521187
1.0%
51933
0.8%
501498
1.3%
491780
1.5%
481495
1.3%
471677
1.4%
461570
1.3%
451940
1.6%
442270
1.9%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.798717
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:50.092119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7810701
Coefficient of variation (CV)0.55580908
Kurtosis-1.1870963
Mean15.798717
Median Absolute Deviation (MAD)8
Skewness-0.0021109856
Sum1883365
Variance77.107192
MonotonicityNot monotonic
2025-11-22T13:30:50.139606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
174401
 
3.7%
54310
 
3.6%
154188
 
3.5%
254155
 
3.5%
264141
 
3.5%
94090
 
3.4%
124082
 
3.4%
164071
 
3.4%
24054
 
3.4%
194048
 
3.4%
Other values (21)77670
65.2%
ValueCountFrequency (%)
13620
3.0%
24054
3.4%
33847
3.2%
43760
3.2%
54310
3.6%
63819
3.2%
73658
3.1%
83919
3.3%
94090
3.4%
103569
3.0%
ValueCountFrequency (%)
312207
1.9%
303844
3.2%
293580
3.0%
283942
3.3%
273791
3.2%
264141
3.5%
254155
3.5%
243983
3.3%
233612
3.0%
223593
3.0%

stays_in_weekend_nights
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9270531
Minimum0
Maximum19
Zeros51895
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:50.188092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.99511703
Coefficient of variation (CV)1.0734197
Kurtosis6.3653972
Mean0.9270531
Median Absolute Deviation (MAD)1
Skewness1.3202425
Sum110514
Variance0.9902579
MonotonicityNot monotonic
2025-11-22T13:30:50.227518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
051895
43.5%
233266
27.9%
130615
25.7%
41847
 
1.5%
31252
 
1.1%
6152
 
0.1%
577
 
0.1%
858
 
< 0.1%
719
 
< 0.1%
910
 
< 0.1%
Other values (7)19
 
< 0.1%
ValueCountFrequency (%)
051895
43.5%
130615
25.7%
233266
27.9%
31252
 
1.1%
41847
 
1.5%
577
 
0.1%
6152
 
0.1%
719
 
< 0.1%
858
 
< 0.1%
910
 
< 0.1%
ValueCountFrequency (%)
191
 
< 0.1%
181
 
< 0.1%
162
 
< 0.1%
141
 
< 0.1%
132
 
< 0.1%
125
 
< 0.1%
107
 
< 0.1%
910
 
< 0.1%
858
< 0.1%
719
 
< 0.1%

stays_in_week_nights
Real number (ℝ)

Zeros 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4991947
Minimum0
Maximum50
Zeros7572
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:50.278290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8971058
Coefficient of variation (CV)0.75908683
Kurtosis22.250866
Mean2.4991947
Median Absolute Deviation (MAD)1
Skewness2.7548629
Sum297929
Variance3.5990103
MonotonicityNot monotonic
2025-11-22T13:30:50.327584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
233670
28.2%
130292
25.4%
322241
18.7%
511068
 
9.3%
49543
 
8.0%
07572
 
6.4%
61494
 
1.3%
101030
 
0.9%
71024
 
0.9%
8654
 
0.5%
Other values (23)622
 
0.5%
ValueCountFrequency (%)
07572
 
6.4%
130292
25.4%
233670
28.2%
322241
18.7%
49543
 
8.0%
511068
 
9.3%
61494
 
1.3%
71024
 
0.9%
8654
 
0.5%
9228
 
0.2%
ValueCountFrequency (%)
501
 
< 0.1%
421
 
< 0.1%
402
 
< 0.1%
341
 
< 0.1%
331
 
< 0.1%
321
 
< 0.1%
304
< 0.1%
261
 
< 0.1%
256
< 0.1%
243
< 0.1%

adults
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8592064
Minimum0
Maximum55
Zeros223
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:50.369587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.57518558
Coefficient of variation (CV)0.30937155
Kurtosis1392.5063
Mean1.8592064
Median Absolute Deviation (MAD)0
Skewness18.774333
Sum221636
Variance0.33083845
MonotonicityNot monotonic
2025-11-22T13:30:50.418936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
289680
75.2%
123027
 
19.3%
36202
 
5.2%
0223
 
0.2%
462
 
0.1%
265
 
< 0.1%
272
 
< 0.1%
202
 
< 0.1%
52
 
< 0.1%
401
 
< 0.1%
Other values (4)4
 
< 0.1%
ValueCountFrequency (%)
0223
 
0.2%
123027
 
19.3%
289680
75.2%
36202
 
5.2%
462
 
0.1%
52
 
< 0.1%
61
 
< 0.1%
101
 
< 0.1%
202
 
< 0.1%
265
 
< 0.1%
ValueCountFrequency (%)
551
 
< 0.1%
501
 
< 0.1%
401
 
< 0.1%
272
 
< 0.1%
265
 
< 0.1%
202
 
< 0.1%
101
 
< 0.1%
61
 
< 0.1%
52
 
< 0.1%
462
0.1%

children
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
0.0
110620 
1.0
 
4861
2.0
 
3652
3.0
 
76
10.0
 
1

Length

Max length4
Median length3
Mean length3.0000084
Min length3

Characters and Unicode

Total characters357631
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0110620
92.8%
1.04861
 
4.1%
2.03652
 
3.1%
3.076
 
0.1%
10.01
 
< 0.1%

Length

2025-11-22T13:30:50.476217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:50.515904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0110620
92.8%
1.04861
 
4.1%
2.03652
 
3.1%
3.076
 
0.1%
10.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0229831
64.3%
.119210
33.3%
14862
 
1.4%
23652
 
1.0%
376
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)357631
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0229831
64.3%
.119210
33.3%
14862
 
1.4%
23652
 
1.0%
376
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)357631
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0229831
64.3%
.119210
33.3%
14862
 
1.4%
23652
 
1.0%
376
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)357631
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0229831
64.3%
.119210
33.3%
14862
 
1.4%
23652
 
1.0%
376
 
< 0.1%

babies
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
0
118293 
1
 
900
2
 
15
10
 
1
9
 
1

Length

Max length2
Median length1
Mean length1.0000084
Min length1

Characters and Unicode

Total characters119211
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0118293
99.2%
1900
 
0.8%
215
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%

Length

2025-11-22T13:30:50.563670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:50.607646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0118293
99.2%
1900
 
0.8%
215
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0118294
99.2%
1901
 
0.8%
215
 
< 0.1%
91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)119211
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0118294
99.2%
1901
 
0.8%
215
 
< 0.1%
91
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)119211
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0118294
99.2%
1901
 
0.8%
215
 
< 0.1%
91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)119211
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0118294
99.2%
1901
 
0.8%
215
 
< 0.1%
91
 
< 0.1%

meal
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
BB
92236 
HB
14458 
SC
10549 
Undefined
 
1169
FB
 
798

Length

Max length9
Median length2
Mean length2.0686436
Min length2

Characters and Unicode

Total characters246603
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB92236
77.4%
HB14458
 
12.1%
SC10549
 
8.8%
Undefined1169
 
1.0%
FB798
 
0.7%

Length

2025-11-22T13:30:50.653565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:50.688662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bb92236
77.4%
hb14458
 
12.1%
sc10549
 
8.8%
undefined1169
 
1.0%
fb798
 
0.7%

Most occurring characters

ValueCountFrequency (%)
B199728
81.0%
H14458
 
5.9%
S10549
 
4.3%
C10549
 
4.3%
n2338
 
0.9%
d2338
 
0.9%
e2338
 
0.9%
U1169
 
0.5%
f1169
 
0.5%
i1169
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)246603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B199728
81.0%
H14458
 
5.9%
S10549
 
4.3%
C10549
 
4.3%
n2338
 
0.9%
d2338
 
0.9%
e2338
 
0.9%
U1169
 
0.5%
f1169
 
0.5%
i1169
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)246603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B199728
81.0%
H14458
 
5.9%
S10549
 
4.3%
C10549
 
4.3%
n2338
 
0.9%
d2338
 
0.9%
e2338
 
0.9%
U1169
 
0.5%
f1169
 
0.5%
i1169
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)246603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B199728
81.0%
H14458
 
5.9%
S10549
 
4.3%
C10549
 
4.3%
n2338
 
0.9%
d2338
 
0.9%
e2338
 
0.9%
U1169
 
0.5%
f1169
 
0.5%
i1169
 
0.5%

country
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size7.7 MiB

market_segment
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
Online TA
56408 
Offline TA/TO
24182 
Groups
19791 
Direct
12582 
Corporate
 
5282
Other values (3)
 
965

Length

Max length13
Median length9
Mean length9.0191762
Min length6

Characters and Unicode

Total characters1075176
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowOnline TA

Common Values

ValueCountFrequency (%)
Online TA56408
47.3%
Offline TA/TO24182
20.3%
Groups19791
 
16.6%
Direct12582
 
10.6%
Corporate5282
 
4.4%
Complementary728
 
0.6%
Aviation235
 
0.2%
Undefined2
 
< 0.1%

Length

2025-11-22T13:30:50.743555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:50.779094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
online56408
28.2%
ta56408
28.2%
offline24182
12.1%
ta/to24182
12.1%
groups19791
 
9.9%
direct12582
 
6.3%
corporate5282
 
2.6%
complementary728
 
0.4%
aviation235
 
0.1%
undefined2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n137965
12.8%
O104772
9.7%
T104772
9.7%
e99914
9.3%
i93644
8.7%
l81318
7.6%
A80825
7.5%
80590
7.5%
f48366
 
4.5%
r43665
 
4.1%
Other values (16)199345
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1075176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n137965
12.8%
O104772
9.7%
T104772
9.7%
e99914
9.3%
i93644
8.7%
l81318
7.6%
A80825
7.5%
80590
7.5%
f48366
 
4.5%
r43665
 
4.1%
Other values (16)199345
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1075176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n137965
12.8%
O104772
9.7%
T104772
9.7%
e99914
9.3%
i93644
8.7%
l81318
7.6%
A80825
7.5%
80590
7.5%
f48366
 
4.5%
r43665
 
4.1%
Other values (16)199345
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1075176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n137965
12.8%
O104772
9.7%
T104772
9.7%
e99914
9.3%
i93644
8.7%
l81318
7.6%
A80825
7.5%
80590
7.5%
f48366
 
4.5%
r43665
 
4.1%
Other values (16)199345
18.5%

distribution_channel
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.0 MiB
TA/TO
97750 
Direct
14611 
Corporate
 
6651
GDS
 
193
Undefined
 
5

Length

Max length9
Median length5
Mean length5.3426642
Min length3

Characters and Unicode

Total characters636899
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO97750
82.0%
Direct14611
 
12.3%
Corporate6651
 
5.6%
GDS193
 
0.2%
Undefined5
 
< 0.1%

Length

2025-11-22T13:30:50.840717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:50.868851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ta/to97750
82.0%
direct14611
 
12.3%
corporate6651
 
5.6%
gds193
 
0.2%
undefined5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T195500
30.7%
/97750
15.3%
O97750
15.3%
A97750
15.3%
r27913
 
4.4%
e21272
 
3.3%
t21262
 
3.3%
D14804
 
2.3%
i14616
 
2.3%
c14611
 
2.3%
Other values (10)33671
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)636899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T195500
30.7%
/97750
15.3%
O97750
15.3%
A97750
15.3%
r27913
 
4.4%
e21272
 
3.3%
t21262
 
3.3%
D14804
 
2.3%
i14616
 
2.3%
c14611
 
2.3%
Other values (10)33671
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)636899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T195500
30.7%
/97750
15.3%
O97750
15.3%
A97750
15.3%
r27913
 
4.4%
e21272
 
3.3%
t21262
 
3.3%
D14804
 
2.3%
i14616
 
2.3%
c14611
 
2.3%
Other values (10)33671
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)636899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T195500
30.7%
/97750
15.3%
O97750
15.3%
A97750
15.3%
r27913
 
4.4%
e21272
 
3.3%
t21262
 
3.3%
D14804
 
2.3%
i14616
 
2.3%
c14611
 
2.3%
Other values (10)33671
 
5.3%

is_repeated_guest
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
0
115455 
1
 
3755

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0115455
96.9%
13755
 
3.1%

Length

2025-11-22T13:30:50.917670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:50.952324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0115455
96.9%
13755
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0115455
96.9%
13755
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0115455
96.9%
13755
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0115455
96.9%
13755
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0115455
96.9%
13755
 
3.1%

previous_cancellations
Real number (ℝ)

Skewed  Zeros 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087190672
Minimum0
Maximum26
Zeros112731
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:50.979980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84491826
Coefficient of variation (CV)9.6904663
Kurtosis673.22115
Mean0.087190672
Median Absolute Deviation (MAD)0
Skewness24.443924
Sum10394
Variance0.71388687
MonotonicityNot monotonic
2025-11-22T13:30:51.021075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0112731
94.6%
16048
 
5.1%
2114
 
0.1%
365
 
0.1%
2448
 
< 0.1%
1135
 
< 0.1%
431
 
< 0.1%
2626
 
< 0.1%
2525
 
< 0.1%
622
 
< 0.1%
Other values (5)65
 
0.1%
ValueCountFrequency (%)
0112731
94.6%
16048
 
5.1%
2114
 
0.1%
365
 
0.1%
431
 
< 0.1%
519
 
< 0.1%
622
 
< 0.1%
1135
 
< 0.1%
1312
 
< 0.1%
1414
 
< 0.1%
ValueCountFrequency (%)
2626
< 0.1%
2525
< 0.1%
2448
< 0.1%
211
 
< 0.1%
1919
 
< 0.1%
1414
 
< 0.1%
1312
 
< 0.1%
1135
< 0.1%
622
< 0.1%
519
 
< 0.1%

previous_bookings_not_canceled
Real number (ℝ)

Skewed  Zeros 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1370942
Minimum0
Maximum72
Zeros115597
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:51.067814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4981372
Coefficient of variation (CV)10.927794
Kurtosis766.95283
Mean0.1370942
Median Absolute Deviation (MAD)0
Skewness23.539555
Sum16343
Variance2.244415
MonotonicityNot monotonic
2025-11-22T13:30:51.118560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0115597
97.0%
11538
 
1.3%
2580
 
0.5%
3333
 
0.3%
4229
 
0.2%
5181
 
0.2%
6113
 
0.1%
788
 
0.1%
870
 
0.1%
959
 
< 0.1%
Other values (63)422
 
0.4%
ValueCountFrequency (%)
0115597
97.0%
11538
 
1.3%
2580
 
0.5%
3333
 
0.3%
4229
 
0.2%
5181
 
0.2%
6113
 
0.1%
788
 
0.1%
870
 
0.1%
959
 
< 0.1%
ValueCountFrequency (%)
721
< 0.1%
711
< 0.1%
701
< 0.1%
691
< 0.1%
681
< 0.1%
671
< 0.1%
661
< 0.1%
651
< 0.1%
641
< 0.1%
631
< 0.1%

reserved_room_type
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
A
85873 
D
19179 
E
 
6519
F
 
2894
G
 
2092
Other values (4)
 
2653

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119210
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A85873
72.0%
D19179
 
16.1%
E6519
 
5.5%
F2894
 
2.4%
G2092
 
1.8%
B1115
 
0.9%
C931
 
0.8%
H601
 
0.5%
L6
 
< 0.1%

Length

2025-11-22T13:30:51.174510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:51.217294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a85873
72.0%
d19179
 
16.1%
e6519
 
5.5%
f2894
 
2.4%
g2092
 
1.8%
b1115
 
0.9%
c931
 
0.8%
h601
 
0.5%
l6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A85873
72.0%
D19179
 
16.1%
E6519
 
5.5%
F2894
 
2.4%
G2092
 
1.8%
B1115
 
0.9%
C931
 
0.8%
H601
 
0.5%
L6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A85873
72.0%
D19179
 
16.1%
E6519
 
5.5%
F2894
 
2.4%
G2092
 
1.8%
B1115
 
0.9%
C931
 
0.8%
H601
 
0.5%
L6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A85873
72.0%
D19179
 
16.1%
E6519
 
5.5%
F2894
 
2.4%
G2092
 
1.8%
B1115
 
0.9%
C931
 
0.8%
H601
 
0.5%
L6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A85873
72.0%
D19179
 
16.1%
E6519
 
5.5%
F2894
 
2.4%
G2092
 
1.8%
B1115
 
0.9%
C931
 
0.8%
H601
 
0.5%
L6
 
< 0.1%

assigned_room_type
Categorical

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
A
74020 
D
25309 
E
7798 
F
 
3751
G
 
2549
Other values (6)
 
5783

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119210
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A74020
62.1%
D25309
 
21.2%
E7798
 
6.5%
F3751
 
3.1%
G2549
 
2.1%
C2370
 
2.0%
B2154
 
1.8%
H712
 
0.6%
I359
 
0.3%
K187
 
0.2%

Length

2025-11-22T13:30:51.267691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a74020
62.1%
d25309
 
21.2%
e7798
 
6.5%
f3751
 
3.1%
g2549
 
2.1%
c2370
 
2.0%
b2154
 
1.8%
h712
 
0.6%
i359
 
0.3%
k187
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A74020
62.1%
D25309
 
21.2%
E7798
 
6.5%
F3751
 
3.1%
G2549
 
2.1%
C2370
 
2.0%
B2154
 
1.8%
H712
 
0.6%
I359
 
0.3%
K187
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A74020
62.1%
D25309
 
21.2%
E7798
 
6.5%
F3751
 
3.1%
G2549
 
2.1%
C2370
 
2.0%
B2154
 
1.8%
H712
 
0.6%
I359
 
0.3%
K187
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A74020
62.1%
D25309
 
21.2%
E7798
 
6.5%
F3751
 
3.1%
G2549
 
2.1%
C2370
 
2.0%
B2154
 
1.8%
H712
 
0.6%
I359
 
0.3%
K187
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A74020
62.1%
D25309
 
21.2%
E7798
 
6.5%
F3751
 
3.1%
G2549
 
2.1%
C2370
 
2.0%
B2154
 
1.8%
H712
 
0.6%
I359
 
0.3%
K187
 
0.2%

booking_changes
Real number (ℝ)

Zeros 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21879876
Minimum0
Maximum18
Zeros101232
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:51.312605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.63850446
Coefficient of variation (CV)2.9182271
Kurtosis63.437992
Mean0.21879876
Median Absolute Deviation (MAD)0
Skewness5.5000578
Sum26083
Variance0.40768794
MonotonicityNot monotonic
2025-11-22T13:30:51.351731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0101232
84.9%
112666
 
10.6%
23780
 
3.2%
3914
 
0.8%
4367
 
0.3%
5115
 
0.1%
661
 
0.1%
729
 
< 0.1%
814
 
< 0.1%
98
 
< 0.1%
Other values (9)24
 
< 0.1%
ValueCountFrequency (%)
0101232
84.9%
112666
 
10.6%
23780
 
3.2%
3914
 
0.8%
4367
 
0.3%
5115
 
0.1%
661
 
0.1%
729
 
< 0.1%
814
 
< 0.1%
98
 
< 0.1%
ValueCountFrequency (%)
181
 
< 0.1%
172
 
< 0.1%
162
 
< 0.1%
153
 
< 0.1%
143
 
< 0.1%
135
< 0.1%
121
 
< 0.1%
111
 
< 0.1%
106
< 0.1%
98
< 0.1%

deposit_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
No Deposit
104461 
Non Refund
14587 
Refundable
 
162

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1192100
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit104461
87.6%
Non Refund14587
 
12.2%
Refundable162
 
0.1%

Length

2025-11-22T13:30:51.403153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:51.435069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no104461
43.8%
deposit104461
43.8%
non14587
 
6.1%
refund14587
 
6.1%
refundable162
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o223509
18.7%
e119372
10.0%
N119048
10.0%
119048
10.0%
s104461
8.8%
i104461
8.8%
t104461
8.8%
p104461
8.8%
D104461
8.8%
n29336
 
2.5%
Other values (7)59482
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1192100
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o223509
18.7%
e119372
10.0%
N119048
10.0%
119048
10.0%
s104461
8.8%
i104461
8.8%
t104461
8.8%
p104461
8.8%
D104461
8.8%
n29336
 
2.5%
Other values (7)59482
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1192100
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o223509
18.7%
e119372
10.0%
N119048
10.0%
119048
10.0%
s104461
8.8%
i104461
8.8%
t104461
8.8%
p104461
8.8%
D104461
8.8%
n29336
 
2.5%
Other values (7)59482
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1192100
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o223509
18.7%
e119372
10.0%
N119048
10.0%
119048
10.0%
s104461
8.8%
i104461
8.8%
t104461
8.8%
p104461
8.8%
D104461
8.8%
n29336
 
2.5%
Other values (7)59482
 
5.0%

agent
Real number (ℝ)

High correlation  Zeros 

Distinct334
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.889078
Minimum0
Maximum535
Zeros16280
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:51.477684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median9
Q3152
95-th percentile250
Maximum535
Range535
Interquartile range (IQR)145

Descriptive statistics

Standard deviation107.16888
Coefficient of variation (CV)1.4310349
Kurtosis0.50744609
Mean74.889078
Median Absolute Deviation (MAD)9
Skewness1.2985454
Sum8927527
Variance11485.17
MonotonicityNot monotonic
2025-11-22T13:30:51.532641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
931922
26.8%
016280
13.7%
24013922
11.7%
17187
 
6.0%
143633
 
3.0%
73532
 
3.0%
63290
 
2.8%
2502870
 
2.4%
2411721
 
1.4%
281657
 
1.4%
Other values (324)33196
27.8%
ValueCountFrequency (%)
016280
13.7%
17187
 
6.0%
2162
 
0.1%
31336
 
1.1%
447
 
< 0.1%
5330
 
0.3%
63290
 
2.8%
73532
 
3.0%
81514
 
1.3%
931922
26.8%
ValueCountFrequency (%)
5353
 
< 0.1%
53168
0.1%
52735
< 0.1%
52610
 
< 0.1%
5102
 
< 0.1%
50910
 
< 0.1%
5086
 
< 0.1%
50224
 
< 0.1%
4971
 
< 0.1%
49557
< 0.1%

company
Real number (ℝ)

Zeros 

Distinct349
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.7354
Minimum0
Maximum543
Zeros112442
Zeros (%)94.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:51.587557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile40
Maximum543
Range543
Interquartile range (IQR)0

Descriptive statistics

Standard deviation53.830143
Coefficient of variation (CV)5.0142654
Kurtosis37.898876
Mean10.7354
Median Absolute Deviation (MAD)0
Skewness5.9292756
Sum1279767
Variance2897.6843
MonotonicityNot monotonic
2025-11-22T13:30:51.646909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0112442
94.3%
40924
 
0.8%
223784
 
0.7%
67267
 
0.2%
45249
 
0.2%
153213
 
0.2%
174147
 
0.1%
219141
 
0.1%
281138
 
0.1%
154133
 
0.1%
Other values (339)3772
 
3.2%
ValueCountFrequency (%)
0112442
94.3%
61
 
< 0.1%
81
 
< 0.1%
937
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
1214
 
< 0.1%
149
 
< 0.1%
165
 
< 0.1%
181
 
< 0.1%
ValueCountFrequency (%)
5432
 
< 0.1%
5411
 
< 0.1%
5392
 
< 0.1%
5342
 
< 0.1%
5311
 
< 0.1%
5305
 
< 0.1%
5282
 
< 0.1%
52515
< 0.1%
52317
< 0.1%
5217
< 0.1%

days_in_waiting_list
Real number (ℝ)

Zeros 

Distinct127
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3212147
Minimum0
Maximum391
Zeros115517
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:51.697673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.598002
Coefficient of variation (CV)7.5813763
Kurtosis186.89459
Mean2.3212147
Median Absolute Deviation (MAD)0
Skewness11.948868
Sum276712
Variance309.68967
MonotonicityNot monotonic
2025-11-22T13:30:51.752932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0115517
96.9%
39227
 
0.2%
58164
 
0.1%
44141
 
0.1%
31127
 
0.1%
3596
 
0.1%
4694
 
0.1%
6989
 
0.1%
6383
 
0.1%
8780
 
0.1%
Other values (117)2592
 
2.2%
ValueCountFrequency (%)
0115517
96.9%
112
 
< 0.1%
25
 
< 0.1%
359
 
< 0.1%
425
 
< 0.1%
58
 
< 0.1%
615
 
< 0.1%
74
 
< 0.1%
87
 
< 0.1%
916
 
< 0.1%
ValueCountFrequency (%)
39145
< 0.1%
37915
 
< 0.1%
33015
 
< 0.1%
25910
 
< 0.1%
23635
< 0.1%
22410
 
< 0.1%
22361
0.1%
21521
 
< 0.1%
20715
 
< 0.1%
1931
 
< 0.1%

customer_type
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
Transient
89476 
Transient-Party
25088 
Contract
 
4072
Group
 
574

Length

Max length15
Median length9
Mean length10.209295
Min length5

Characters and Unicode

Total characters1217050
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient89476
75.1%
Transient-Party25088
 
21.0%
Contract4072
 
3.4%
Group574
 
0.5%

Length

2025-11-22T13:30:51.803740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:51.833640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
transient89476
75.1%
transient-party25088
 
21.0%
contract4072
 
3.4%
group574
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n233200
19.2%
t147796
12.1%
r144298
11.9%
a143724
11.8%
T114564
9.4%
s114564
9.4%
i114564
9.4%
e114564
9.4%
y25088
 
2.1%
-25088
 
2.1%
Other values (7)39600
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1217050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n233200
19.2%
t147796
12.1%
r144298
11.9%
a143724
11.8%
T114564
9.4%
s114564
9.4%
i114564
9.4%
e114564
9.4%
y25088
 
2.1%
-25088
 
2.1%
Other values (7)39600
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1217050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n233200
19.2%
t147796
12.1%
r144298
11.9%
a143724
11.8%
T114564
9.4%
s114564
9.4%
i114564
9.4%
e114564
9.4%
y25088
 
2.1%
-25088
 
2.1%
Other values (7)39600
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1217050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n233200
19.2%
t147796
12.1%
r144298
11.9%
a143724
11.8%
T114564
9.4%
s114564
9.4%
i114564
9.4%
e114564
9.4%
y25088
 
2.1%
-25088
 
2.1%
Other values (7)39600
 
3.3%

adr
Real number (ℝ)

Zeros 

Distinct8866
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.96909
Minimum-6.38
Maximum5400
Zeros1810
Zeros (%)1.5%
Negative1
Negative (%)< 0.1%
Memory size1.8 MiB
2025-11-22T13:30:51.877553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile39
Q169.5
median94.95
Q3126
95-th percentile193.5
Maximum5400
Range5406.38
Interquartile range (IQR)56.5

Descriptive statistics

Standard deviation50.434007
Coefficient of variation (CV)0.49460092
Kurtosis1022.8267
Mean101.96909
Median Absolute Deviation (MAD)27.95
Skewness10.612728
Sum12155735
Variance2543.589
MonotonicityNot monotonic
2025-11-22T13:30:51.929486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
623754
 
3.1%
752715
 
2.3%
902472
 
2.1%
652418
 
2.0%
801889
 
1.6%
01810
 
1.5%
951661
 
1.4%
1201607
 
1.3%
1001573
 
1.3%
851538
 
1.3%
Other values (8856)97773
82.0%
ValueCountFrequency (%)
-6.381
 
< 0.1%
01810
1.5%
0.261
 
< 0.1%
0.51
 
< 0.1%
114
 
< 0.1%
1.481
 
< 0.1%
1.562
 
< 0.1%
1.61
 
< 0.1%
1.81
 
< 0.1%
212
 
< 0.1%
ValueCountFrequency (%)
54001
< 0.1%
5101
< 0.1%
5081
< 0.1%
451.51
< 0.1%
4501
< 0.1%
4371
< 0.1%
426.251
< 0.1%
4021
< 0.1%
397.381
< 0.1%
3922
< 0.1%

required_car_parking_spaces
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
0
111801 
1
 
7376
2
 
28
3
 
3
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119210
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0111801
93.8%
17376
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Length

2025-11-22T13:30:51.987810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:52.017654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0111801
93.8%
17376
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0111801
93.8%
17376
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0111801
93.8%
17376
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0111801
93.8%
17376
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)119210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0111801
93.8%
17376
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

total_of_special_requests
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57150407
Minimum0
Maximum5
Zeros70201
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-11-22T13:30:52.051028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7928759
Coefficient of variation (CV)1.3873495
Kurtosis1.4926142
Mean0.57150407
Median Absolute Deviation (MAD)0
Skewness1.3490487
Sum68129
Variance0.62865219
MonotonicityNot monotonic
2025-11-22T13:30:52.090853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
070201
58.9%
133183
27.8%
212952
 
10.9%
32494
 
2.1%
4340
 
0.3%
540
 
< 0.1%
ValueCountFrequency (%)
070201
58.9%
133183
27.8%
212952
 
10.9%
32494
 
2.1%
4340
 
0.3%
540
 
< 0.1%
ValueCountFrequency (%)
540
 
< 0.1%
4340
 
0.3%
32494
 
2.1%
212952
 
10.9%
133183
27.8%
070201
58.9%

reservation_status
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
Check-Out
75011 
Canceled
42993 
No-Show
 
1206

Length

Max length9
Median length9
Mean length8.6191175
Min length7

Characters and Unicode

Total characters1027485
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out75011
62.9%
Canceled42993
36.1%
No-Show1206
 
1.0%

Length

2025-11-22T13:30:52.141148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-22T13:30:52.177929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
check-out75011
62.9%
canceled42993
36.1%
no-show1206
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e160997
15.7%
C118004
11.5%
c118004
11.5%
h76217
7.4%
-76217
7.4%
u75011
7.3%
t75011
7.3%
O75011
7.3%
k75011
7.3%
a42993
 
4.2%
Other values (7)135009
13.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1027485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e160997
15.7%
C118004
11.5%
c118004
11.5%
h76217
7.4%
-76217
7.4%
u75011
7.3%
t75011
7.3%
O75011
7.3%
k75011
7.3%
a42993
 
4.2%
Other values (7)135009
13.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1027485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e160997
15.7%
C118004
11.5%
c118004
11.5%
h76217
7.4%
-76217
7.4%
u75011
7.3%
t75011
7.3%
O75011
7.3%
k75011
7.3%
a42993
 
4.2%
Other values (7)135009
13.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1027485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e160997
15.7%
C118004
11.5%
c118004
11.5%
h76217
7.4%
-76217
7.4%
u75011
7.3%
t75011
7.3%
O75011
7.3%
k75011
7.3%
a42993
 
4.2%
Other values (7)135009
13.1%
Distinct926
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2014-10-17 00:00:00
Maximum2017-09-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-22T13:30:52.222015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:52.279073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-11-22T13:30:45.862553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:25.442037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:27.089656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:28.566017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:30.309606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:31.840893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:33.437766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:35.017557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:36.569887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:38.280644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:39.837245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:41.217886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:42.800402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:44.408051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:45.973841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:25.563134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:27.197675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:28.692100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:30.424591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:31.941133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:33.545162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:35.144390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:36.687883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:38.391586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:39.927847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:41.315651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:42.917535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:44.511896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:46.084848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:25.677902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:27.297714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:28.974371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:30.531851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:32.047932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:33.642384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:35.263718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:36.796702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:38.497917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:40.023647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:41.407974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:43.027641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:44.606557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:46.195596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:25.784347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:27.403172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:29.073765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:30.644370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:32.156988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:33.760328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:35.386671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:36.909184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:38.620575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:40.127605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:41.502858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:43.134889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:44.710264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:46.307480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:25.900457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:27.511305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:29.185771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:30.758745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:32.257572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:33.876535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:35.510108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:37.211330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:38.728228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:40.231805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:41.607810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:43.251709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:44.822322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:46.413119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:26.111757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:27.613647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:29.292208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:30.865332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:32.500840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:33.982772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:35.621293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:37.322083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:38.832266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:40.323237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:41.703355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:43.358090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:44.927944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:46.518055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:26.220059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:27.711794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:29.392504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:30.969419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:32.603166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:34.089857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:35.727423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:37.427683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:38.939674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:40.420223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:41.793985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:43.461584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:45.030032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:46.619547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:26.329678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:27.809883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:29.497415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:31.082080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:32.707745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:34.199634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:35.827679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:37.531773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:39.047844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:40.520049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:41.892547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:43.650068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:45.138023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:46.737929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:26.442354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:27.917941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:29.608706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:31.192044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:32.814484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:34.317841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:35.937828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:37.641929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:39.167579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-22T13:30:31.302341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:32.921417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:34.433396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:36.044339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:37.748133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-22T13:30:31.402081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:33.018711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:34.547710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:36.142339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:37.857826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:39.387927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:40.822333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:42.189653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:43.973728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:45.457703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:47.056954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:26.763942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:28.244042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:29.985916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:31.512924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:33.121545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:34.660395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:36.254546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:37.957831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:39.500106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:40.920932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:42.281159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:44.082928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:45.551118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:47.438953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:26.874561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:28.355568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:30.100772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:31.623345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:33.232470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:34.787450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:36.363738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:38.067897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:39.620824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:41.023001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:42.607939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:44.190624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:45.660986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:47.537919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:26.978944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:28.465312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:30.202227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:31.729581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:33.339862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:34.901168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:36.463305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:38.175027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:39.727569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:41.120582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:42.702997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:44.301730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-22T13:30:45.757738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-22T13:30:52.337722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
adradultsagentarrival_date_day_of_montharrival_date_montharrival_date_week_numberarrival_date_yearassigned_room_typebabiesbooking_changeschildrencompanycustomer_typedays_in_waiting_listdeposit_typedistribution_channelhotelis_canceledis_repeated_guestlead_timemarket_segmentmealprevious_bookings_not_canceledprevious_cancellationsrequired_car_parking_spacesreservation_statusreserved_room_typestays_in_week_nightsstays_in_weekend_nightstotal_of_special_requests
adr1.0000.2760.0580.0270.0010.0740.0000.0000.0000.0080.000-0.1890.000-0.0400.0070.0000.0000.0000.0000.0130.0000.000-0.143-0.1500.0000.0000.0000.0930.0510.197
adults0.2761.0000.1050.0010.0100.0270.0150.0000.000-0.0810.000-0.2880.090-0.0370.0000.0080.0140.0130.0000.1900.0080.000-0.210-0.0370.0000.0080.0040.1530.1270.162
agent0.0580.1051.0000.0010.076-0.0220.0840.1150.0220.0110.058-0.3510.1120.0150.1050.1580.6860.0600.0490.0670.1870.163-0.155-0.1270.0870.0460.1210.2140.1800.087
arrival_date_day_of_month0.0270.0010.0011.0000.0580.0610.0440.0090.0050.0120.010-0.0030.0320.0320.0540.0280.0250.0210.0180.0080.0330.039-0.002-0.0120.0080.0230.010-0.016-0.0070.003
arrival_date_month0.0010.0100.0760.0581.0000.8010.4290.0280.0160.0120.0690.0670.1030.0600.1010.0690.0700.0690.0730.1320.0880.0890.0170.0320.0180.0650.0480.0370.0460.053
arrival_date_week_number0.0740.027-0.0220.0610.8011.0000.4240.0270.0140.0080.062-0.0260.106-0.0040.0950.0640.0680.0650.0740.1130.0810.080-0.0440.0870.0170.0610.0440.0260.0260.019
arrival_date_year0.0000.0150.0840.0440.4290.4241.0000.0530.0090.0290.0440.0620.2130.0740.0520.0270.0430.0260.0090.1040.1590.1120.0250.0520.0180.0230.0820.0150.0290.091
assigned_room_type0.0000.0000.1150.0090.0280.0270.0531.0000.0440.0320.3040.0210.0900.0290.1920.0950.3910.2010.0600.0620.1180.1130.0040.0080.0920.1440.7440.0410.0470.067
babies0.0000.0000.0220.0050.0160.0140.0090.0441.0000.0280.0250.0000.0150.0000.0230.0290.0490.0340.0070.0070.0340.0150.0000.0000.0200.0240.0400.0000.0100.060
booking_changes0.008-0.0810.0110.0120.0120.0080.0290.0320.0281.0000.0240.0940.038-0.0190.0570.0410.0490.0790.001-0.0070.0290.0120.031-0.0730.0260.0570.0180.0630.0390.042
children0.0000.0000.0580.0100.0690.0620.0440.3040.0250.0241.0000.0240.0610.0180.0730.0430.0460.0280.0340.0280.1000.0370.0020.0000.0300.0280.3570.0130.0280.061
company-0.189-0.288-0.351-0.0030.067-0.0260.0620.0210.0000.0940.0241.0000.093-0.0350.1310.3190.1240.0840.168-0.1720.3180.0650.3630.0500.0240.0610.030-0.101-0.116-0.111
customer_type0.0000.0900.1120.0320.1030.1060.2130.0900.0150.0380.0610.0931.0000.0780.0980.0800.0520.1370.1060.1220.2750.1390.0140.0100.0410.0970.1090.0800.0890.097
days_in_waiting_list-0.040-0.0370.0150.0320.060-0.0040.0740.0290.000-0.0190.018-0.0350.0781.0000.1280.0270.0870.0680.0240.1530.0780.062-0.0190.1160.0340.0500.0280.012-0.075-0.123
deposit_type0.0070.0000.1050.0540.1010.0950.0520.1920.0230.0570.0730.1310.0980.1281.0000.0910.1770.4820.0580.2730.3740.0930.0130.0510.0710.3470.1520.0470.0730.220
distribution_channel0.0000.0080.1580.0280.0690.0640.0270.0950.0290.0410.0430.3190.0800.0270.0911.0000.1870.1770.3000.1160.6920.0780.1080.0510.0760.1290.1000.0070.0550.070
hotel0.0000.0140.6860.0250.0700.0680.0430.3910.0490.0490.0460.1240.0520.0870.1770.1871.0000.1370.0520.0950.1470.3170.0170.0500.2210.1370.3230.1930.1990.046
is_canceled0.0000.0130.0600.0210.0690.0650.0260.2010.0340.0790.0280.0840.1370.0680.4820.1770.1371.0000.0840.2810.2670.0500.0410.0440.1981.0000.0720.0280.0220.266
is_repeated_guest0.0000.0000.0490.0180.0730.0740.0090.0600.0070.0010.0340.1680.1060.0240.0580.3000.0520.0841.0000.1330.3490.0620.3220.1860.0790.0850.0370.0170.0810.041
lead_time0.0130.1900.0670.0080.1320.1130.1040.0620.007-0.0070.028-0.1720.1220.1530.2730.1160.0950.2810.1331.0000.1700.089-0.1890.1710.0570.2070.0510.2960.162-0.074
market_segment0.0000.0080.1870.0330.0880.0810.1590.1180.0340.0290.1000.3180.2750.0780.3740.6920.1470.2670.3490.1701.0000.1920.0970.0550.0930.1960.1350.0330.0620.210
meal0.0000.0000.1630.0390.0890.0800.1120.1130.0150.0120.0370.0650.1390.0620.0930.0780.3170.0500.0620.0890.1921.0000.0140.0880.0270.0400.1020.0460.0620.062
previous_bookings_not_canceled-0.143-0.210-0.155-0.0020.017-0.0440.0250.0040.0000.0310.0020.3630.014-0.0190.0130.1080.0170.0410.322-0.1890.0970.0141.0000.1010.0190.0290.004-0.119-0.0840.025
previous_cancellations-0.150-0.037-0.127-0.0120.0320.0870.0520.0080.000-0.0730.0000.0500.0100.1160.0510.0510.0500.0440.1860.1710.0550.0880.1011.0000.0000.0310.007-0.062-0.055-0.130
required_car_parking_spaces0.0000.0000.0870.0080.0180.0170.0180.0920.0200.0260.0300.0240.0410.0340.0710.0760.2210.1980.0790.0570.0930.0270.0190.0001.0000.1400.0790.0170.0150.044
reservation_status0.0000.0080.0460.0230.0650.0610.0230.1440.0240.0570.0280.0610.0970.0500.3470.1290.1371.0000.0850.2070.1960.0400.0290.0310.1401.0000.0520.0300.0240.189
reserved_room_type0.0000.0040.1210.0100.0480.0440.0820.7440.0400.0180.3570.0300.1090.0280.1520.1000.3230.0720.0370.0510.1350.1020.0040.0070.0790.0521.0000.0470.0570.075
stays_in_week_nights0.0930.1530.214-0.0160.0370.0260.0150.0410.0000.0630.013-0.1010.0800.0120.0470.0070.1930.0280.0170.2960.0330.046-0.119-0.0620.0170.0300.0471.0000.2370.076
stays_in_weekend_nights0.0510.1270.180-0.0070.0460.0260.0290.0470.0100.0390.028-0.1160.089-0.0750.0730.0550.1990.0220.0810.1620.0620.062-0.084-0.0550.0150.0240.0570.2371.0000.080
total_of_special_requests0.1970.1620.0870.0030.0530.0190.0910.0670.0600.0420.061-0.1110.097-0.1230.2200.0700.0460.2660.041-0.0740.2100.0620.025-0.1300.0440.1890.0750.0760.0801.000

Missing values

2025-11-22T13:30:47.772058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-22T13:30:48.281482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
0Resort Hotel03422015July2710020.00BBPRTDirectDirect000CC3No Deposit0.00.00Transient0.000Check-Out2015-07-01
1Resort Hotel07372015July2710020.00BBPRTDirectDirect000CC4No Deposit0.00.00Transient0.000Check-Out2015-07-01
2Resort Hotel072015July2710110.00BBGBRDirectDirect000AC0No Deposit0.00.00Transient75.000Check-Out2015-07-02
3Resort Hotel0132015July2710110.00BBGBRCorporateCorporate000AA0No Deposit304.00.00Transient75.000Check-Out2015-07-02
4Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.00.00Transient98.001Check-Out2015-07-03
5Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.00.00Transient98.001Check-Out2015-07-03
6Resort Hotel002015July2710220.00BBPRTDirectDirect000CC0No Deposit0.00.00Transient107.000Check-Out2015-07-03
7Resort Hotel092015July2710220.00FBPRTDirectDirect000CC0No Deposit303.00.00Transient103.001Check-Out2015-07-03
8Resort Hotel1852015July2710320.00BBPRTOnline TATA/TO000AA0No Deposit240.00.00Transient82.001Canceled2015-05-06
9Resort Hotel1752015July2710320.00HBPRTOffline TA/TOTA/TO000DD0No Deposit15.00.00Transient105.500Canceled2015-04-22
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
119380City Hotel0442017August35311320.00SCDEUOnline TATA/TO000AA0No Deposit9.00.00Transient140.7501Check-Out2017-09-04
119381City Hotel01882017August35312320.00BBDEUDirectDirect000AA0No Deposit14.00.00Transient99.0000Check-Out2017-09-05
119382City Hotel01352017August35302430.00BBJPNOnline TATA/TO000GG0No Deposit7.00.00Transient209.0000Check-Out2017-09-05
119383City Hotel01642017August35312420.00BBDEUOffline TA/TOTA/TO000AA0No Deposit42.00.00Transient87.6000Check-Out2017-09-06
119384City Hotel0212017August35302520.00BBBELOffline TA/TOTA/TO000AA0No Deposit394.00.00Transient96.1402Check-Out2017-09-06
119385City Hotel0232017August35302520.00BBBELOffline TA/TOTA/TO000AA0No Deposit394.00.00Transient96.1400Check-Out2017-09-06
119386City Hotel01022017August35312530.00BBFRAOnline TATA/TO000EE0No Deposit9.00.00Transient225.4302Check-Out2017-09-07
119387City Hotel0342017August35312520.00BBDEUOnline TATA/TO000DD0No Deposit9.00.00Transient157.7104Check-Out2017-09-07
119388City Hotel01092017August35312520.00BBGBROnline TATA/TO000AA0No Deposit89.00.00Transient104.4000Check-Out2017-09-07
119389City Hotel02052017August35292720.00HBDEUOnline TATA/TO000AA0No Deposit9.00.00Transient151.2002Check-Out2017-09-07

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealmarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date# duplicates
5785City Hotel12772016November4671220.00BBGroupsTA/TO000AA0Non Refund0.00.00Transient100.000Canceled2016-04-04180
4562City Hotel1682016February8170220.00BBGroupsTA/TO010AA0Non Refund37.00.00Transient75.000Canceled2016-01-06150
5456City Hotel11882016June25150210.00BBOffline TA/TOTA/TO000AA0Non Refund119.00.039Transient130.000Canceled2016-01-18110
5260City Hotel11582016May22240210.00BBGroupsTA/TO000AA0Non Refund37.00.031Transient130.000Canceled2016-01-18101
4231City Hotel1342015December5080210.00BBOffline TA/TOTA/TO010AA0Non Refund19.00.00Transient90.000Canceled2015-11-17100
4171City Hotel1282017March920320.00BBGroupsTA/TO000AA0Non Refund0.00.00Transient95.000Canceled2017-02-0299
4287City Hotel1382017January2140110.00BBCorporateCorporate000AA0Non Refund0.067.00Transient75.000Canceled2016-12-0799
5253City Hotel11562017April17260320.00BBGroupsTA/TO000AA0Non Refund37.00.00Transient100.000Canceled2016-11-2199
4586City Hotel1712016June25140310.00BBOffline TA/TOTA/TO000AA0Non Refund236.00.00Transient120.000Canceled2016-04-2789
5320City Hotel11662016November4510310.00BBOffline TA/TOTA/TO000AA0Non Refund236.00.00Transient110.000Canceled2016-07-1385